Dr. Leanne Archer is a Researcher in the School of Geographical Sciences at the University of Bristol, specializing in hydrology and climate change impacts. Her work focuses on flood risk assessment in Small Island Developing States, extreme rainfall events, and the application of convection-permitting climate models. She collaborates with experts like Prof. Paul Bates and Dr. Jonty Rougier on interdisciplinary projects addressing tropical cyclone hazards and climate adaptation strategies. Her research interests include analyzing flood exposure in vulnerable regions, soil moisture dynamics in urban flooding, and improving global flood forecasts for humanitarian operations. Archer’s publications emphasize the implications of climate change on rainfall-driven disasters, particularly in Puerto Rico and East Africa, using high-resolution climate projections to evaluate future risks under 1.5° C and 2° C warming scenarios. Recent work explores innovations like the Surface Water Ocean Topography Mission for flood modeling and evaluates the suitability of TanDEM-X data for inundation studies in island nations. Her contributions bridge environmental science with policy, aiming to enhance disaster preparedness and resilience in climate-sensitive regions.
Bei Wang Phillips is an Associate Professor in the School of Computing and a faculty member at the Scientific Computing and Imaging (SCI) Institute at the University of Utah. She holds a Ph.D. in Computer Science from Duke University and an undergraduate degree from the University of Bridgeport. Her research focuses on Topological Data Analysis (TDA), data visualization, computational topology, and machine learning, with applications in scientific data exploration and analysis. She has received prestigious awards including the NSF CAREER Award (2022) and the PECASE Award (2025). Her work spans projects funded by NSF, NIH, and DOE, including multiparameter TDA and topology-aware data compression. She advises numerous students and collaborates on interdisciplinary initiatives in astrophysics, climate science, and AI fairness. Education: Ph.D. in Computer Science, Duke University (2010) B.S. in Computer Science and Mathematics, University of Bridgeport (2003) Research Interests: Topological techniques for large-scale data analysis Integration of topological, geometric, and machine learning methods Applications in visualization, bioinformatics, and network analysis Key Projects: NSF-funded TDA research (DMS-2301361, OAC-2313124) DOE project on topology-preserving data compression Collaborations with NASA, Argonne National Lab, and Carnegie Institution of Washington Awards: Presidential Early Career Award for Scientists and Engineers (2025) NSF CAREER Award (2022) DOE Early Career Research Program (2020) Advising and Grants: Mentored over 30 students and postdocs Recipient of multiple NSF and DOE grants totaling millions
Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk
Elmira Hassanzadeh is an Associate Professor at the Department of Civil, Geological and Mining Engineering , Polytechnique Montréal, with a focus on integrated water resource modeling and management under climate and anthropogenic changes. She is affiliated with the Global Institute for Water Security (University of Saskatchewan) and the Experimental and Digital Water Flow Engineering Group (GENIE EAU) . Education: PhD in Civil Engineering, University of Saskatchewan Postdoctoral Fellow, McGill University Research interests include climate change impact assessment, socio-hydrological modeling, stakeholder engagement in water management, and sustainable development of water systems in cold regions. Her work emphasizes decision-making under uncertainty and adaptive strategies for ungauged basins. Recent publications (2024–2020) span coastal flood hazard modeling, climate-driven hydrological changes in tropical and Canadian watersheds, evaluation of agricultural practices under warming climates, and integrated approaches to water quality and system dynamics. Keywords include Hydrology , Climate Change , Water Security , and Socio-Hydrology . Supervision: She has mentored 15+ graduate students in projects related to Lake Urmia, Saskatchewan River Basin, and climate adaptation in Africa and the Pacific.
Lorenzo M. Polvani is the Maurice Ewing and J. Lamar Worzel Professor of Geophysics at Columbia University, with dual appointments in the Department of Applied Physics and Applied Mathematics and the Department of Earth and Environmental Sciences. He has taught at Columbia for over 30 years and received multiple teaching awards. PhD in Physical Oceanography, MIT/Woods Hole Joint Program (1988) MSc and BSc in Physics, McGill University His research spans Atmospheric Science , Climate Modeling , and Arctic Studies , focusing on geophysical fluid dynamics, radiative forcing, and climate sensitivity. Recent work examines nonlinear climate responses to CO2 variations, Arctic amplification mechanisms, and volcanic aerosol impacts. Scientific publications reveal trends in radiative forcing , Arctic sea ice dynamics , stratospheric ozone effects , and climate feedback interactions . Key tools include the ClimKern Python package for radiative feedback analysis. Notable research includes: Nonlinear precipitation responses to volcanic eruptions High CO2 forcing effects on North Atlantic Oscillation Cloud feedbacks reducing hydrological sensitivity Arctic amplification through radiative-advective equilibrium Stratospheric ozone impacts on tropical climate patterns
Sai Ravela is a Principal Research Scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT). His research focuses on nonlinear stochastic dynamics, coherent fluid systems, uncertainty quantification, and autonomous observing technologies. He specializes in developing data-driven methodologies for natural hazard detection, climate change impacts, and environmental risk assessment. Ravela’s work integrates computational science with geophysical applications, including storm surge modeling, extreme rainfall analysis, and geothermal exploration. He pioneers techniques like neural dynamical systems and adversarial learning to improve predictive accuracy in nonstationary climate regimes. His contributions span environmental monitoring systems, autonomous aircraft resilience frameworks, and policy-informed climate vulnerability assessments. Key research areas include: Coastal flood risk in Bangladesh and Vietnam Dynamic data-driven applications systems (DDDAS) Machine learning for geosciences and environmental systems Uncertainty quantification in complex fluid dynamics He leads interdisciplinary projects at MIT’s Computational Science and Engineering (CSE) program, advancing methods for data assimilation, surrogate modeling, and real-time environmental observatories. His innovations bridge theoretical frameworks with practical solutions for climate adaptation and disaster resilience.
Thomas Smith is an Associate Professor in Environmental Geography at the London School of Economics (LSE), specializing in wildland fires, tropical environmental change, and peatland management. He joined LSE in 2018 after lecturing at King’s College London and has held visiting fellowships at institutions including the National University of Singapore and Monash University Malaysia. His research focuses on biomass burning’s role in the Earth system, particularly greenhouse gas emissions from tropical peatlands and savannas. Smith’s expertise includes infrared spectroscopy, wildfire modeling, and land management decision support. He emphasizes interdisciplinary approaches to address fire emissions, agricultural practices, and their environmental impacts. Notable awards include being Highly Commended for Research Guidance & Support at the LSESU Teaching Excellence Awards 2019. His recent work involves field experiments (e.g., GAMBUT project) and citizen science initiatives to measure air pollution. Collaborative research spans global wildfire dynamics, climate change impacts, and policy interventions in Southeast Asia’s haze season. Smith contributes to both academic discourse and practical solutions for sustainable land use and climate resilience.
Markus Reichstein is a Professor for Global Geoecology at Friedrich Schiller University (FSU) Jena and Director of the Biogeochemical Integration Department at the Max Planck Institute for Biogeochemistry. His research focuses on ecosystem responses to climate variability, climate extremes, and the application of AI in Earth system science. He holds a PhD in Plant Ecology from the University of Bayreuth and has pioneered interdisciplinary approaches combining machine learning with environmental modeling. Key roles include leadership in the Michael-Stifel-Center Jena for Data-driven and Simulation Science and founding director of the ELLIS Unit Jena. He contributed to the IPCC Special Report on Climate Extremes and has received prestigious awards such as the Leibniz Prize. His work bridges ecology, hydrology, and atmospheric science, addressing critical global challenges like carbon cycle feedbacks and ecosystem resilience. Recent research emphasizes AI-driven early warning systems for climate risks, integrating observational data with mechanistic models. His team explores land-atmosphere interactions, soil-vegetation dynamics, and the impacts of climate extremes on societal systems. Notable projects include GartenDiv, a citizen science initiative for garden biodiversity, and advancements in global water cycle modeling using hybrid AI-physics frameworks. Awards include the Piers J. Sellers Award (2018), ERC Synergy Grant (2019), and Leibniz Prize (2020). He collaborates with international networks like ELLIS and Future Earth, advancing data-driven solutions for sustainability science.
Sarah Kang is the Director of the Department of Climate Dynamics at the Max Planck Institute for Meteorology in Hamburg, Germany, a position she has held since August 2023. She leads the Director's Research Group (CDY) focusing on fundamental climate dynamics. Prior to this, she served as Professor in the Department of Urban and Environmental Engineering at Ulsan National Institute of Science and Technology (UNIST) in South Korea from 2011-2023, progressing through assistant, associate, and full professor ranks. Her research examines complex climate system dynamics, with emphasis on: Large-scale atmosphere and ocean circulation patterns Tropical-extratropical climate interactions Hydrological cycle responses to climate change Mechanisms of polar amplification Teleconnections between ocean basins and climate zones Analysis of her recent publications reveals dominant research themes: Ocean-atmosphere coupling mechanisms Radiative forcing and climate sensitivity Hemispheric climate asymmetries Tropical precipitation dynamics Polar warming impacts on global circulation with consistent methodology employing high-resolution climate modeling and observational verification. Major scientific recognitions include: AGU Atmospheric Sciences Ascent Award (2022) AOGS Kamide Lecture Award (2018) Editor's Citation for Excellence in Refereeing (GRL 2018) UNIST Teaching Excellence Award (2012) NCAR Advanced Studies Fellowship (2009) She maintains extensive professional engagement as: Co-chair of CLIVAR Climate Dynamics Panel Science Steering Committee member for CFMIP Associate Editor for Frontiers in Climate Editor for AGU Advances Board member of Korean Meteorological Society
Mariam Zachariah serves as a Research Fellow at the Centre for Environmental Policy within the Faculty of Natural Sciences at Imperial College London. She is a core contributor to World Weather Attribution (WWA), an international scientific collaboration conducting rapid climate change attribution analyses for extreme weather events globally. Her work bridges climate science, vulnerability assessment, and policy-relevant research. Her educational foundation includes a PhD from the Indian Institute of Technology Bombay (IITB), where she investigated climate impacts on Indian agriculture. This research focused on drought and extreme temperature effects on crop yields in major agrarian regions, recognizing agriculture's critical role in India's climate-vulnerable economy. Zachariah's research centers on near-real-time attribution of extreme events to quantify human-induced climate change influences. Her expertise spans climate modeling, statistical analysis of extreme weather, and integrating vulnerability frameworks to assess compound impacts on communities. She examines how climate change interacts with socioeconomic factors to exacerbate disasters, particularly in agricultural systems and flood-prone regions worldwide. Analysis of her recent publications reveals a dominant focus on rapid attribution of droughts, floods, and heatwaves across diverse global contexts - from the Horn of Africa to Central Europe and South America. These studies consistently demonstrate climate change as a significant amplifier of event severity, while emphasizing how pre-existing vulnerabilities determine actual impacts. Her work increasingly addresses compound hazards and the intersection of climate change with infrastructure failures and land management. As a key member of the World Weather Attribution initiative, Zachariah collaborates with climate scientists, social scientists, and vulnerability experts in a unique operational framework that delivers scientific assessments within days of extreme events. This work directly informs policymakers, media, and affected communities about climate change's role in contemporary disasters.
Jonathan L. Rogers is a Professor in the Accounting Department at Leeds School of Business, University of Colorado Boulder. He maintains his office in Koelbel Building, room 433, and can be contacted at jonathan.rogers@colorado.edu or by phone at 303-735-6620. Dr. Rogers received dual bachelor's degrees from the University of Texas in 1996: one in Business Administration with a focus in finance, and another in Economics with a minor in accounting. He earned his PhD in Accounting from the Wharton School of the University of Pennsylvania in 2005. He is also a certified management accountant and certified in financial management, though both certifications are currently inactive. Dr. Rogers' research focuses on voluntary disclosure, market microstructure, multinational firms, insider trading, and stock return volatility . His work has been published in all three top accounting journals (Journal of Accounting Research, Journal of Accounting and Economics, The Accounting Review) and has received significant attention from major media outlets including The Wall Street Journal, The New York Times, Financial Times, Fortune, Reuters, Bloomberg TV, and CNBC. His research has also been cited by members of Congress. His recent publications span a diverse range of topics from accounting and finance to meteorology and healthcare, reflecting interdisciplinary collaborations. Major themes in his work include financial disclosure practices, market microstructure, insider trading, and the dissemination of financial information. His research on SEC dissemination in high-frequency trading environments has been particularly influential in both academic and regulatory circles. 2015 EKS&H Faculty Fellowship 2015 RAST Conference Best Paper Award 2011 Fama-Miller Center Research Grant 2010 William Ladany Faculty Scholar 2009 Ernest R. Wish Award 2009 Initiative on Global Markets Research Grant 2003 Deloitte Foundation Doctoral Fellowship European Accounting Association's 2003 Doctoral Colloquium Fellowship 2001 Geewax, Terker & Company Prize for Investment Research Dr. Rogers serves on the editorial board of the Journal of Accounting Research and works as an ad hoc reviewer for the Journal of Finance, the Accounting Review, the Journal of Accounting and Economics, the Review of Accounting Studies, Contemporary Accounting Research, American Accounting Association Midyear, and the Annual and FARS section meetings. His research has been supported by numerous grants including those from the Fama-Miller Center, Initiative on Global Markets, and the Deloitte Foundation. While specific information about his laboratory or research team is not provided in the available text, his extensive publication record and editorial roles suggest he likely collaborates with multiple researchers and potentially supervises graduate students in accounting research.
Erhan Kutanoglu is an Associate Professor in the Operations Research and Industrial Engineering Graduate Program at The University of Texas at Austin's Cockrell School of Engineering. He joined the faculty in 2002 and received a National Science Foundation Early Career Development Award that year. His research focuses on integrating predictive models with stochastic optimization to address challenges in disaster resilience, humanitarian logistics, and semiconductor manufacturing. Key areas include hurricane mitigation, power grid resilience, and supply chain optimization. Education: PhD in Industrial Engineering from Lehigh University (1999). Research Interests: Applied operations research for manufacturing/service logistics, disaster resilience decision-making, semiconductor cycle time optimization, and inventory modeling. Recent work emphasizes hurricane evacuation planning, flood mitigation for critical infrastructure, and equity considerations in grid resilience. Publications: Over 50 peer-reviewed articles in journals like IEEE Transactions, European Journal of Operational Research, and Annals of Operations Research. Notable work includes models for power grid resilience, patient evacuation strategies, and semiconductor manufacturing efficiency. Awards: NSF CAREER Award (2002), recognized for contributions to service logistics optimization and stochastic modeling. Advising & Grants: Advised graduate students on projects involving hurricane preparedness and semiconductor scheduling. Active in collaborative research with industry partners to streamline manufacturing processes and enhance disaster response systems. Labs/Teams: Engaged with the Cockrell School's infrastructure resilience research groups and interdisciplinary teams addressing climate adaptation challenges.
Hyuck Jin Park is a Full Professor in the Department of Energy Resources and Geosystems Engineering at Sejong University, South Korea, where he has been teaching and conducting research since 2003. With a Ph.D. in Engineering Geology from Purdue University, his expertise spans geotechnical engineering, landslide analysis, and geospatial technologies. Professor Park has built a distinguished career in landslide hazard assessment, combining traditional geotechnical approaches with modern machine learning techniques to improve prediction accuracy and risk management. His educational background includes: B.S. in Geology from Yonsei University (1990) M.S. in Geophysics from Yonsei University (1993) Ph.D. in Engineering Geology from Purdue University (2011) Professor Park's research focuses on the spatial and temporal probability of landslide occurrence, utilizing fuzzy logic, probabilistic analysis, GIS, Monte Carlo simulation, and machine learning for landslide hazard assessment. His work integrates physically based models with statistical approaches to better understand landslide mechanisms and improve prediction capabilities. He has made significant contributions to the development of methodologies that account for geological uncertainties in hazard assessment, with applications ranging from rock slope stability to rainfall-induced shallow landslides. His recent publications demonstrate a clear trend toward integrating explainable artificial intelligence with traditional geotechnical approaches for natural hazard assessment. Professor Park's work increasingly focuses on making machine learning models transparent and interpretable while maintaining high predictive accuracy. The research spans multiple hazard types including landslides, earthquakes, and floods, with a growing emphasis on climate change impacts and data-scarce environments. With an h-index of 28 and over 3,421 citations, Professor Park has established himself as a leading researcher in his field. His work has been published in high-impact journals including Engineering Geology, Landslides, and Catena, reflecting the significance and quality of his contributions to geotechnical engineering and natural hazard assessment. Professor Park has mentored numerous researchers through collaborative projects and has secured funding for his innovative work in landslide prediction and hazard assessment. His research has involved significant international collaboration, particularly with researchers from Malaysia, Australia, and Yemen, addressing landslide and flood risks in diverse geographical contexts. He leads research activities within the Department of Geoinformation Engineering at Sejong University and has contributed to the development of specialized tools like DEWS (Distance, Elevation, Watershed, and Slope unit) for landslide early warning systems.
Mingfang Ting is a Professor of Climate at the Columbia Climate School , affiliated with the Lamont-Doherty Earth Observatory . She co-directs the M.S. in Climate program and serves as Co-Senior Director for Education at the Columbia Climate School. Her research focuses on climate variability, extremes, Asian monsoons, Arctic sea ice, and climate change impacts on agriculture and health. Education: Ph.D. in Climate Dynamics (Princeton University, 1990), M.S. and B.S. from Peking University (1985, 1983). She has taught climate science at Columbia since 2004. Research Interests: Investigates weather/climate extremes in a warming world, Asian monsoon dynamics, Arctic sea ice variability, decadal climate modes, and hydroclimate impacts. Recent work emphasizes heatwaves, drought-flood linkages, and climate model projections. Key Projects (2020–2023): Advancing predictive understanding of North American drought Asian monsoon response to climate change Causal mechanisms of dry/humid heat extremes Arctic transport pathways and sea ice loss Scientific Contributions: Authored/co-authored over 200+ peer-reviewed articles. Her 2022 analysis of the 2021 North American heatwave and 2022 Pakistan floods exemplifies climate attribution science. Awards: AMS Distinguished Scientific Award (2021) AGU Fellow (2022) NSF CAREER Award (1995) Reuters' World’s Top Climate Scientists (2021) Leadership: Former Co-Editor-in-Chief of Journal of Climate (2020–2023). Active in climate education and global resilience initiatives through the Decarbonization Network. Labs/Teams: Leads interdisciplinary teams at LDEO focusing on climate dynamics and extreme event analysis. Collaborates internationally on monsoon systems and Arctic research.
David M. Higdon is a Professor and Department Head of the Department of Statistics at Virginia Tech within the College of Science. He specializes in Bayesian statistical modeling of environmental and physical systems, focusing on integrating physical observations with computer simulations for prediction and inference. Previously, he spent 14 years at Los Alamos National Laboratory as a scientist and group leader in the Statistical Sciences Group. Education: Ph.D. in Statistics, University of Washington, 1994 M.A. in Mathematics, University of California San Diego, 1989 B.A. in Mathematics, University of California San Diego, 1987 Research Interests: Higdon’s work spans space-time modeling , inverse problems in hydrology and imaging , statistical modeling in ecology and environmental science , and multiscale models . He develops methods for parallel processing in posterior exploration , statistical computing , and Monte Carlo simulations . His research addresses critical challenges in uncertainty quantification (UQ), including climate modeling, nuclear density functional theory, and geophysical imaging. Publications Trends: His recent articles emphasize Bayesian methodologies applied to complex systems, such as climate forecasting, materials science, and cosmology. A recurring theme is the development of emulators and surrogate models to handle computationally intensive simulations. Awards: Fellow of the American Statistical Association Advising & Grants: While no specific advisees are listed, Higdon has contributed to interdisciplinary collaborations in UQ and statistical modeling. His work has been supported by grants from agencies such as the National Science Foundation and Department of Energy. Labs/Teams: He leads the Statistics Department’s efforts in UQ and computational statistics, fostering collaborations across engineering, environmental science, and physics.
